Early detection of disease or malignant tissue can lead to a better prognosis. The development of non-invasive methods for detection and characterization of tumors and other anomalies has an extreme importance in current medicine. Dynamic, contrast-enhanced imaging provides an effective means of monitoring non-invasively and with high spatial and/or temporal resolutions the microvascular properties of tumors and tissues. The increased permeability of tumor vasculature gives rise to increased leakage of tracers including contrast agents, and enables characterization of enhancement patterns in the tissue. One method for characterization of tumor microvasculature is dynamic, contrast-enhanced (DCE) magnetic resonance imaging (MRI), or DCE-MRI. For DCE-MRI, multi-slice images are typically acquired before, during, and after the contrast agent infusion, resulting in the acquisition of a time sequence of image volumes, also referred to as a dynamic, contrast-enhanced image dataset.
Dynamic, contrast-enhanced image datasets can be post-processed using image analysis software to create supplemental data for interpretation by a radiologist. Such data can illustrate diagnostically important criterion that can not be evident from the original grayscale images. Examples of such supplemental data can include parametric maps, time-enhancement curve shape estimations, and/or multi-parametric analysis results. Some examples of image post-processing analysis techniques for creating such supplemental data can be seen in Breast MRI: Fundamentals and Technical Aspects, Hendrick, R. Edward, 2008, XVI, pp. 171-186 and U.S. Published Patent Application No. 2009/0190806, entitled “METHOD FOR TRACKING OF CONTRAST ENHANCEMENT PATTERN FOR PHARMACOKINETIC AND PARAMETRIC ANALYSIS IN FAST-ENHANCING TISSUES USING HIGH-RESOLUTION MRI,” the contents of which are fully incorporated herein by way of useful background information.
Many image post-processing analysis techniques require identification of the arrival of contrast media administrated via the arterial/capillary system to the organ/tissue of interest. Contrast media arrival can vary widely depending on multiple factors, such as the speed and site of injection, location of organ/tissue in the body, patient blood flow patterns, etc. In many DCE image post-processing analyses, the time moment when contrast media concentration (and therefore, image signal intensity) achieves peak in major blood vessels or arteries close to the tissue/organ of interest signifies a key time point in a time array to be used for image post-processing analysis. In such cases, correct identification of contrast media arrival is required to generate correct diagnostic interpretation data.
Applicant's commonly assigned, co-pending U.S. patent application Ser. No. 12/797,934, entitled AUTOMATED CONTRAST MEDIA ARRIVAL DETECTION METHOD FOR DYNAMIC CONTRAST ENHANCED MRI, by Naira Muradyan—the teachings of which are incorporated herein by reference as useful background information—discloses automated methods suitable for detecting contrast arrival in various tissues, such as breasts, lungs or prostates. However, such methods rely on the presence of large vessels within the field of view in order to detect the arrival event. Methods disclosed in the '934 application are inaccurate for detecting arrival when executed on image datasets lacking such features. Examples of tissues that may lack such features include upper brain structures, hands and/or fingers, feet, unilateral or bilateral breast with limited imaging field (e.g., excludes aorta), prostate or female pelvis (cervix, uterus) with smaller imaging field (e.g., excludes iliac arteries), or unilateral lung with imaging field that excludes the aorta.